Abstract:
In recent years, there has been a growing emphasis on the Environmental, Social, and
Governance (ESG) approach to evaluate the long-term sustainability and social viability
of corporations. This research proposes developing an automated framework that
uses a company’s financial components to predict its ESG score via machine learning
and multivariate time series methods. Most renowned companies have now started to
report their yearly ESG ratings and with time the number of companies reporting ESG
components is likely to increase. Consequently, there exists a wide area of research
with a lot of potential due to the availability of ESG data, which can be analyzed for a
better understanding of a company’s portfolio. Our findings show that machine learning
methods achieved a minimum RMSE of 13.856 and MAPE of 32.731%, while time
series forecasting methods achieved a minimum RMSE of 4.237 and MAPE of 8.494%.
The Consumer Staples and Health Care industries were most predictable using machine
learning, while Consumer Staples and Real Estate were most predictable using time
series methods. MENAP and South America were the most predictable regions with
both methods. These results can help up build an automated predictive framework that
can aid financial analysts, portfolio managers, and policymakers in enhancing ESG score
predictions and improving decision-making processes.
